def test_lnlikelihood(self):
t = np.arange(0, 1, 0.01)
S = np.random.random(t.size)
theta = self.random_theta(3, 2)
kursl = KurSL(theta)
model = ModelWrapper(kursl)
lnlikelihood = KurslMCMC.lnlikelihood(theta, t, S[:-1], model)
self.assertTrue(lnlikelihood < 0, "Any negative value is good")
def test_lnlikelihood_zero(self):
t = np.arange(0, 1, 0.01)
S = 2 * np.cos(10 * t + 0) + 6 * np.cos(20 * t + 4)
theta = np.array([
[10, 0, 2, 0],
[20, 4, 6, 0],
])
kursl = KurSL(theta)
model = ModelWrapper(kursl)
lnlikelihood = KurslMCMC.lnlikelihood(theta, t, S[:-1], model)
self.assertEqual(np.round(lnlikelihood, 10), 0,
"Exact reconstruction should return 0")
self.assertTrue(model.THRESHOLD_OBTAINED, "0 is below any threshold")